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[Bugfix] Reduce _npu_flash_attention mask to 128x128 for memory savings #1100

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[Bugfix] Reduce _npu_flash_attention mask to 128x128 for memory savings #1100

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8 changes: 5 additions & 3 deletions vllm_ascend/worker/model_runner_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -219,7 +219,7 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
weakref.proxy(self))
self.attn_mask_builder = AttentionMaskBuilder(
min(self.model_config.max_model_len,
int(os.getenv("PAGED_ATTENTION_MASK_LEN", 10000))), self.dtype)
int(os.getenv("PAGED_ATTENTION_MASK_LEN", 128))), self.dtype)

# Set up speculative decoding.
self.use_aux_hidden_state_outputs = False
Expand Down Expand Up @@ -834,9 +834,11 @@ def _make_attention_mask(self, seq_lens, query_lens, position,
seq_lens, query_lens, position, self.dtype, self.device)
# Prefill without cache situation.
elif attn_state == AscendAttentionState.PrefillNoCache:
max_seq_len = max(seq_lens, default=0)
# Note: `torch_npu._npu_flash_attention` only requires a 128x128 mask, so we hardcode it here.
# Once a new attention operator for prefill-only state is added,
# the mask generation logic here must be updated according to the new operator used.
return self.attn_mask_builder.get_attn_mask(
max_seq_len, self.dtype, self.device)
128, self.dtype, self.device)
# Prefill with cache hit.
elif attn_state == AscendAttentionState.PrefillCacheHit:
return self.attn_mask_builder.get_attn_mask(
Expand Down
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